Chapter 6

Maps

Note

This Chapter does not have Exercises. The code and examples below show map plotting in R with tidyverse and sf with examples from India.

Code

library(tidyverse)     # everything and ggplot2
library(sf)            # sf and shape files
library(ggthemes)      # for theme_map
library(ggspatial)     # map annotations

6.2 Simple features maps

First, we download a shape file of India’s map (from here) and plot a map of India with latest state and Union Territory boundaries. The code below shows a simple example of ease of plotting with ggplot() and geom_sf() along with labeling official names of States and Union Territories.

Code

# Reading in the shape file
india_map <- read_sf(here::here("data", 
                                "india_map", 
                                "India_State_Boundary.shp")) |> 
  # tidy names of variables
  rename(state = State_Name) |> 
  # Renaming to official names. Official names taken from Government:
  # https://knowindia.india.gov.in/states-uts/
  mutate(state = case_when(
    state == "Andaman & Nicobar" ~ "Andaman and Nicobar Islands",
    state == "Daman and Diu and Dadra and Nagar Haveli" ~ "Dadra and Nagar Haveli and Daman & Diu",
    state == "Jammu and Kashmir" ~ "Jammu & Kashmir",
    state == "Telengana" ~ "Telangana",
    .default = state
  ))

# Add names of Union Territories
union_territories <- c(
  "Andaman and Nicobar Islands",
  "Chandigarh",
  "Dadra and Nagar Haveli and Daman & Diu",
  "Delhi",
  "Jammu & Kashmir",
  "Ladakh",
  "Lakshadweep",
  "Puducherry"
)


india_map |> 
  mutate(type = if_else(state %in% union_territories,
                        "Union Territory",
                        "State")) |> 
  ggplot(aes(geometry = geometry,
             col = type)) + 
  geom_sf(col = "darkgrey", fill = "white") +
  geom_sf_text(aes(label = state,
                size = type)) +
  scale_color_manual(values = c("darkblue", "darkred")) +
  scale_size_discrete(range = c(3.5, 2.5)) +
  theme_map() +
  theme(legend.position = "bottom") +
  labs(col = NULL) +
  annotation_scale(bar_cols = c("darkgrey", "white"),
                   location = "br") +
  annotation_north_arrow(location = "tr", 
                         which_north = "true") +
  guides(size = "none")

6.4 Working with sf data

Plotting a specific state from i..e, Haryana, and its districts along with their area (in thousand sq. km.) using official data from Survey of India, and adding geoms from other metadata - area and length of district borders: –

Code

haryana_map <- read_sf(here::here("data",
                                  "haryana_map",
                                  "HARYANA_DISTRICT_BDY.shp")) |> 
  janitor::clean_names() |> 
  mutate(
    district = str_replace_all(district,
                               pattern = ">",
                               replacement = "A"),
    state = str_replace_all(state,
                            pattern = ">",
                            replacement = "A"),
    district = case_when(
      district == "FAR|DABAD" ~ "FARIDABAD",
      district == "J|ND" ~ "JIND",
      district == "PAN|PAT" ~ "PANIPAT",
      district == "SON|PAT" ~ "SONIPAT",
      .default = district
    ),
    district = snakecase::to_title_case(district)
      )

haryana_map |>
  select(-c(remarks, state_lgd, district_l)) |> 
  arrange(desc(shape_area)) |> 
  mutate(
    rank = row_number(),
    area = round(shape_area/1e9, 2),
    font_col = district %in% c("Hisar", "Sirsa")
  ) |> 
  
  ggplot(aes(geometry = geometry,
             label = paste0(district),
             fill = shape_area/1e6)) +
  geom_sf(col = "white") +
  geom_sf_text(aes(col = font_col),
               size = 4) +
  theme_map() +
  annotation_scale(location = "br") +
  annotation_north_arrow(location = "tr") +
  labs(fill = "District Area\n(in sq. km.)",
       title = "Haryana: Districts (with area)") +
  scale_fill_viridis_c(labels = scales::label_comma(),
                       option = "G") +
  scale_color_manual(values = c("white", "black")) +
  guides(col = "none")

Chloropleth with tmap

A package to create chloropleths very easily is the tmap package (Tennekes 2018) of R . However, is uses a very different syntax than ggplot2 .

Code

library(tmap)
tm_hy <- haryana_map |> 
  mutate(area = shape_area/1e6)

t_hy <- tm_shape(tm_hy) +
  tm_polygons(col = "area",
              title = "District Area\n(in sq. km.)",
              border.col = "black",
              interactive = TRUE,
              style = "pretty")
t_hy

An interesting feature is the ability to plot interactive maps: –

Code

tmap_mode("view")
tmap_last()

Code

tmap_mode("plot")

Adding leaflet maps to Chloropleths

Code

library(leaflet)

# Create a quantile of colours' palette to be used
pal_hy <- colorQuantile("Blues", 
                        domain = NULL,
                        n = 5)

# Create vector of text to display on pop-ups in leaflet map
p_popup <- paste0(haryana_map$district, 
                  " District.  Area: ", 
                  round(haryana_map$shape_area/1e6, 0),
                  " sq. km.")

# Create leaflet map
# Data set
haryana_map |> 
  
  # Transform polygons into CRS=4326 since leaflet only understand that
  st_transform(crs = 4326) |> 
  
  # Begin leaflet map
  leaflet() |> 
  
  # Add polygons from the geometry column of the data-set
  addPolygons(
    stroke = FALSE,                       # remove polygon borders
    fillColor = ~ pal_hy(shape_area/1e6), # set fill color with function
    fillOpacity = 0.6,                    # translucent to see background map
    smoothFactor = 0.5,                   # make it nicer
    popup = p_popup,                      # add popup
    group = "District Area"               # a Group label for leaflet options
  ) |> 
  
  # Add base map from leaflet; default is Open Street Maps
  addTiles() |>                           # Adding Base Map
  
  # Adding a legend
  addLegend(
    position = "bottomright",  # location
    pal = pal_hy,              # palette function
    values = ~shape_area/1e6,  # value to be passed to palette function
    title = "District Area (sq. km.)" # legend title
    ) |> 
  
  # Adding an option to view different base maps
  addLayersControl(
    baseGroups = c("OSM", "Carto"),
    overlayGroups = c("District Area")
  )

References

Tennekes, Martijn. 2018. “Tmap: Thematic Maps in r” 84. https://doi.org/10.18637/jss.v084.i06.